Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,778
2	New Jersey	19,839
3	Rhode Island	16,276
4	Massachusetts	16,089
5	Louisiana	15,509
6	Arizona	15,494
7	District of Columbia	15,131
8	Connecticut	13,241
9	Delaware	12,868
10	Illinois	12,009
11	Maryland	11,915
12	Mississippi	11,286
13	Florida	10,834
14	Nebraska	10,679
15	Iowa	10,559
16	Alabama	10,028
17	South Carolina	9,845
18	Georgia	9,295
19	Arkansas	8,632
20	Utah	8,618
21	South Dakota	8,292
22	Tennessee	8,277
23	Texas	8,277
24	Nevada	8,134
25	Virginia	7,965
26	California	7,681
27	Pennsylvania	7,626
28	North Carolina	7,594
29	Indiana	7,550
30	Michigan	7,534
31	Minnesota	7,128
32	New Mexico	6,796
33	Wisconsin	6,416
34	Kansas	6,189
35	Colorado	6,182
36	Idaho	5,370
37	North Dakota	5,346
38	Washington	5,301
39	Ohio	5,246
40	Oklahoma	4,674
41	Missouri	4,448
42	New Hampshire	4,392
43	Kentucky	4,157
44	Wyoming	3,065
45	Puerto Rico	2,774
46	Oregon	2,654
47	Maine	2,593
48	West Virginia	2,134
49	Alaska	2,079
50	Vermont	2,038
51	Montana	1,371
52	Hawaii	785

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	514
2	Florida	407
3	Louisiana	406
4	Texas	352
5	Alabama	292
6	South Carolina	279
7	Mississippi	261
8	Idaho	256
9	Tennessee	245
10	Georgia	241
11	Nevada	229
12	California	216
13	Utah	205
14	Arkansas	198
15	Oklahoma	179
16	Iowa	163
17	North Carolina	149
18	Missouri	133
19	New Mexico	118
20	Wisconsin	116
21	Kansas	108
22	Nebraska	104
23	Ohio	96
24	Minnesota	94
25	North Dakota	94
26	South Dakota	87
27	Maryland	84
28	Kentucky	82
29	Delaware	81
30	Washington	81
31	Rhode Island	79
32	District of Columbia	77
33	Colorado	74
34	Virginia	73
35	Illinois	72
36	West Virginia	71
37	Montana	67
38	Pennsylvania	63
39	Indiana	62
40	Oregon	62
41	Michigan	61
42	Wyoming	57
43	Alaska	54
44	Massachusetts	36
45	New York	32
46	New Jersey	28
47	Puerto Rico	28
48	Hawaii	23
49	Connecticut	21
50	Maine	15
51	New Hampshire	14
52	Vermont	11

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,739
2	New York	1,643
3	Connecticut	1,219
4	Massachusetts	1,199
5	Rhode Island	919
6	District of Columbia	804
7	Louisiana	721
8	Michigan	628
9	Illinois	578
10	Maryland	543
11	Pennsylvania	538
12	Delaware	530
13	Indiana	406
14	Mississippi	404
15	Colorado	296
16	New Hampshire	284
17	Arizona	281
18	Georgia	271
19	Minnesota	270
20	Ohio	257
21	New Mexico	254
22	Iowa	235
23	Virginia	226
24	Alabama	217
25	Florida	186
26	Nevada	186
27	Washington	185
28	Missouri	178
29	South Carolina	175
30	California	172
31	Nebraska	151
32	Kentucky	143
33	North Carolina	141
34	Wisconsin	141
35	North Dakota	116
36	South Dakota	114
37	Texas	104
38	Oklahoma	103
39	Arkansas	102
40	Tennessee	102
41	Kansas	100
42	Vermont	89
43	Maine	82
44	Utah	63
45	Idaho	56
46	Oregon	55
47	West Virginia	53
48	Puerto Rico	49
49	Wyoming	36
50	Montana	23
51	Alaska	20
52	Hawaii	13

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	10
2	Arizona	9
3	New Jersey	8
4	South Carolina	5
5	Alabama	4
6	Louisiana	4
7	Nevada	4
8	Rhode Island	4
9	California	3
10	District of Columbia	3
11	Florida	3
12	Massachusetts	3
13	Texas	3
14	Virginia	3
15	Illinois	2
16	Indiana	2
17	Maryland	2
18	New Mexico	2
19	North Carolina	2
20	Ohio	2
21	Pennsylvania	2
22	Tennessee	2
23	Arkansas	1
24	Delaware	1
25	Georgia	1
26	Idaho	1
27	Iowa	1
28	Kentucky	1
29	Michigan	1
30	Minnesota	1
31	Missouri	1
32	Nebraska	1
33	New Hampshire	1
34	New York	1
35	Oregon	1
36	South Dakota	1
37	Utah	1
38	Washington	1
39	Alaska	0
40	Colorado	0
41	Connecticut	0
42	Hawaii	0
43	Kansas	0
44	Maine	0
45	Montana	0
46	North Dakota	0
47	Oklahoma	0
48	Puerto Rico	0
49	Vermont	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	133,375	1	99
Lake	Tennessee	99,344	2	99
Lee	Arkansas	92,243	3	99
Dakota	Nebraska	90,133	4	99
Buena Vista	Iowa	88,022	5	99
Davidson	Tennessee	19,362	148	95
Richland	South Carolina	10,751	451	85
Orange	California	6,955	803	74
York	South Carolina	5,997	955	69
Pierce	Washington	3,719	1468	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,282	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	224	675	78
Davidson	Tennessee	199	734	76
Pierce	Washington	123	997	68
Orange	California	118	1027	67
York	South Carolina	43	1635	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.